Email scraping and deduplication process

A lot of people screw up data enrichment. And not in small ways...in big ways. I've walked into instances where: 😱 Recent sales-entered data was being overwritten by stale enrichment data 😱 Instead of setting up an integration, a massive file was imported all at once, into standard fields, without a data back-up....leaving no audit trail and losing historical data 😱 Enrichment was set up to trigger every time a record was created or updated in Salesforce, creating a situation where only 1,000 records or less could be updated at one time without hitting the Salesforce API limits 😱 Enrichment data wasn't standardized, so each vendor was entering in different formats for fields like employee size So, how do you set it up correctly? Here's what it should look like... Typical steps included: 1. Input Stage: Define the entry points for raw data (e.g., web forms, imports, email captures). 2. Cleaning Stage: Build workflows to: 🔺 Standardize formats (e.g., phone numbers, dates, addresses). 🔺 Correct invalid or missing data (e.g., normalize country names to ISO codes). 🔺 Remove duplicates based on unique identifiers (e.g., email or account ID). 3. Enrichment Stage: 🔺 Match records with external datasets to fill gaps. 🔺 Append metadata (e.g., confidence scores, enrichment source). 4. Output Stage: Push cleaned and enriched data back into your CRM or database. Example washing machine flow: 1. Input: New leads enter from web forms or imports. 2. Cleaning: 🔺 Deduplicate by email or company domain. 🔺 Standardize phone numbers to E.164 format. 🔺 Normalize country names to ISO codes. 3. Enrichment: 🔺 Call Clearbit API to append industry, company size, and LinkedIn URL. 🔺 Validate emails with an email verification tool. 4. Output: Push cleaned and enriched data back to CRM, tagging it with the enrichment source and date. Things to consider: 🔻 Typically, you want to enter enrichment data into separate custom fields. This is duplicative, but if you don't have really strong audit trails and strong enrichment rules, you shouldn't write into a default field because you could cause confusion and frustration with sales, if you overwrite their recently entered data. 🔻 You need to understand all of the fields you're enriching very intimately...what is their purpose, at which stage do they need to be enriched? Don't be lazy and enrich every field at every record edit, it'll harm your systems and speed-to-lead. 🔻 Make sure any enrichment automation takes race conditions into consideration -- what other automations could be triggered, and how would that impact the API limits/system performance? 🔻 How will you monitor results? Set up reports and audit trails, whether through Snowflake or field history in Salesforce. 🔻 Don't forget about consent management fields! Running out of room....what else? Did you find this helpful? #marketing #sales #marketingoperations #revenueoperations

This one’s long overdue. A few months back, Louis Van Wyk and I shared the workflow that became the backbone of almost every ColdIQ campaign (https://lnkd.in/d7-3Sr8s). A Clay workflow that 3×’d our TAM for every client. When you’re managing 70+ outbound projects, things start to stretch. Lists grow faster than tools can sync. Enrichment flows multiply. Data quality becomes a full-time job. So we built a fix. One unified Clay Workbook, we call it the GrandMaster Template. A single prospecting engine that keeps everything - clean, scalable, and reusable across every GTM workflow. Here’s what the system does step by step 👇 1️⃣ Blocklist first Uploads your client’s do-not-target list → Cleans it → Enriches company names to domains → Blocks all enrichment across every tool for those domains (saves Clay credits) 2️⃣ Lead discovery from multiple sources → Get domains from Ocean.io, DiscoLike, public databases (Instant Data Scraper) → Take those domains to Apollo.io → find people → pull back via Webhook into Clay using LeadMagic → Clay finds more leads on same domains (no dupes) → LinkedIn SalesNav scraped via Prospeo.io finds even more (still no dupes) → All leads centralized in Clay 3️⃣ Layered enrichment → Verified emails (Prospeo.io, LeadMagic, Icypeas, Datagma, Findymail, Hunter.io) → Mobiles (FullEnrich) → Fact-check LinkedIn profiles via LeadMagic API or OpenAI scraper- saves more credits Layering Apollo.io + Clay + LinkedIn = 3× more qualified leads 4️⃣ Verification engine → Email validation (LeadMagic + BounceBan (Verify risky emails without sending messages)) → Phone validation (FullEnrich) 5️⃣ Final output → Deduped → Verified → Sorted by seniority → Top 3 per company → Ready for Instantly.ai, Smartlead, or lemlist Built once. Reused across every client. Kept quality high and chaos low. If you want the full walkthrough comment “Maximize” and I'll send it over.

Just learned something expensive about B2B outbound the hard way. We were losing our pipeline to something most teams don't even think about: bad email hygiene. Here's what was happening - we'd scrape 10,000 leads, validate once through our usual tool, and call it done. Seemed logical, right? Wrong. Those "invalid" emails? A lot of them weren't actually invalid. Our single validation was just missing work emails that existed elsewhere, or flagging personal emails that were perfectly good. So we built something different. A waterfall system that doesn't just validate - it recovers. The process is pretty straightforward: Start with what Apollo gives you (work email, personal email, or nothing). Prioritize the work email, fall back to personal if needed. Run initial validation through Enrichley. But here's the key part - instead of giving up on failures, we treat them as recovery opportunities. For every email that fails, we run conditional lookups through Prospeo.io, then LeadMagic, then Findymail. Only activate the next step if the previous one fails. Merge everything back together and validate one final time. Result? We're now turning 10,000 scraped leads into 9,000+ validated, inbox-ready emails without burning through API credits on every row. The difference in our Smartlead campaigns has been pretty significant. Fewer bounces, better deliverability, and we're not leaving qualified prospects on the table because of lazy validation. Built this with n8n and Clay, but the real insight isn't the tools - it's treating email validation like revenue preservation, not just a checkbox. Anyone else seeing issues with single-pass validation missing good emails? Liked it? Follow Harish Kanna for more insights like this.